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A hybrid active contour model based on global and local information for medical image segmentation

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Abstract

For segmenting medical images with abundant noise, blurry boundaries, and intensity heterogeneities effectively, a hybrid active contour model that synthesizes the global information and the local information is proposed. A novel global energy functional is constructed, together with an adaptive weight by the statistical information of image pixels on the clustering idea. Minimizing this global energy functional in a variational level set formulation will drive the curve to desirable boundaries. The local energy functional contains the local threshold, which is used to correct the deviation of the level set function. Experiments demonstrate that the proposed method can segment synthetic and medical images effectively, and have a relatively higher performance compared to other representative methods.

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Acknowledgements

This work was supported by China Postdoctoral Science Foundation under Grant 2017M621130, Liaoning Provincial Natural Science Fund Guidance Plan under Grant 201602228, Natural Science Foundations of China under Grant 61172108, 61139001, 81241059, 61671105, and 41671439.

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Authors and Affiliations

  1. Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China

    Lingling Fang & Hongyang Zhao

  2. College of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, China

    Tianshuang Qiu

  3. Department of Mathematics, Liaoning Normal University, Dalian City, Liaoning Province, China

    Fang Lv

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  1. Lingling Fang
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  2. Tianshuang Qiu
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  3. Hongyang Zhao
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  4. Fang Lv
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Correspondence to Tianshuang Qiu.

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Fang, L., Qiu, T., Zhao, H. et al. A hybrid active contour model based on global and local information for medical image segmentation. Multidim Syst Sign Process 30, 689–703 (2019). https://doi.org/10.1007/s11045-018-0578-0

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  • DOI: https://doi.org/10.1007/s11045-018-0578-0

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